KR102097953B1 - Failure risk index estimation device and failure risk index estimation method - Google Patents

Abstract

Based on the FMEA results read from the FMEA results DB (2) and the information A for statistical evaluation, the model formula constructing section 4a constructs a model expression representing the transition of the failure risk indicator according to the statistical distribution. Based on the information read by the parameter estimation unit 4b from the FMEA result DB (2) and the predetermined work interval DB (3), the difference between the repair work interval calculated from the model formula and the previously determined repair work interval is the smallest. Ji estimates the parameter values of the model equation statistically.

Description

Failure risk index estimation device and failure risk index estimation method

The present invention relates to a failure risk index estimating apparatus and a failure risk index estimating method for estimating an indicator of a risk of failure in a facility.

The failure mode effect analysis (hereinafter referred to as FMEA) is an analysis method for obtaining a result of assigning a level for each item relating to the failure to the effect that the failure has on the target equipment. The items related to the failure include, for example, the frequency at which the failure occurs in the equipment, and the magnitude of the effect of the failure on the equipment. In addition, since the operation data of the equipment is not sufficiently accumulated, when it is difficult to evaluate the need for maintenance or repair of the equipment from the operation data, the FMEA analysis results may be referred to when evaluating this.

For example, Patent Document 1 describes an operation rate prediction device that predicts an operation rate of a predicted machine system using FMEA. The operation rate predicting apparatus includes a map in which a correspondence relationship between a level of a plurality of evaluation items related to a failure rate and a failure coefficient is defined, and referring to this map, the level of evaluation items of components of the predicted machine system is referred to. Specify the corresponding failure factor. The operation rate predicting apparatus estimates the failure rate of a component from a specific failure coefficient, and predicts the operation rate of the predicted mechanical system based on the failure rate estimated by each of the plurality of components. In addition, the map is fitted to match the performance of the utilization rate of the similar mechanical system.

On the other hand, in the step of accurately evaluating the risk of failure of the target facility, maintenance of the facility is already being performed, and the maintenance work interval is often determined according to the situation of the facility. For this reason, conventionally, the technique of calculating the expected maintenance work interval for each component is proposed based on the failure factor for each component of the target facility.

For example, in the operation management apparatus described in Patent Literature 2, the component rank coefficient of the component is selected in consideration of the failure factor and the effect of the failure of the component in the stationary state, and the predicted life of the component is determined. Considering this, the deterioration rank coefficient of the component is selected. Based on the selected component rank factor and deterioration rank factor, the operation management apparatus calculates an inspection cycle expected for a component part, and determines an effective maintenance cycle of the component part, that is, a maintenance work interval based on the calculated inspection cycle. Doing.

Patent Document 1: Japanese Patent Publication No. 2016-126728 Patent Document 2: Japanese Patent Publication No. 2004-252549

In the operation rate predicting apparatus described in Patent Document 1, in order to obtain the above-described map, performance data sufficient to accurately reproduce the performance of the utilization rate of the mechanical system similar to the predicted mechanical system was required.

In addition, in the above-described operation rate predicting device, only the magnitude relationship of the failure rate between different levels of evaluation items is considered. For example, under the constraint that the failure rate at which the frequency level of failure occurs is lower than the failure rate at which the frequency level of failure occurs, the operation rate is predicted. For this reason, the fluctuation of the failure rate in an actual mechanical system cannot be properly evaluated.

In the operation management apparatus described in Patent Document 2, the effective maintenance period is determined on the premise that the inspection cycle expected for the component is a constant multiple of the current inspection cycle, but this premise is that the risk of failure is linear with respect to the elapsed time. If not, it does not hold.

Since the risk of a failure occurring in an actual facility is generally nonlinear, the operation management device cannot be applied to the actual facility as it is.

The present invention is to solve the above problems, and to obtain a failure risk index estimating apparatus and a failure risk index estimation method capable of properly estimating an indicator of a risk of failure in a facility even when there is no or little maintenance performance data of the facility. It is aimed at.

The failure risk index estimation apparatus according to the present invention includes a model expression construction unit, a parameter estimation unit, a range reduction unit, an index estimation unit, and a merging unit. The model formula construction unit is based on information representing FMEA results for each inspection item of the components constituting the equipment and information indicating a statistical distribution used for estimation of the failure risk indicator, and a model expression representing a transition of the failure risk indicator according to the statistical distribution. To build. The parameter estimating unit calculates the maintenance work interval calculated from the model formula and the previously determined repair work based on the information indicating the FMEA result for each inspection item of the parts constituting the equipment and the information indicating the maintenance work interval already determined for each inspection item of the parts. Statistical estimation of the parameter value of the model equation where the gap difference is the smallest. The range reduction unit includes facility information including equipment, parts constituting the equipment, and inspection items for each component, performance data for maintenance work for each inspection item of the components constituting equipment, and performance data for failures for each component constituting the equipment. Is classified as corresponding information for each failure mode effect analysis result. The indicator estimation unit estimates the estimated value of the failure risk indicator according to the relationship between the performance data of the maintenance work and the performance data of the failure, based on the information classified by the range reduction unit. The merging unit divides the first estimate value, which is the parameter value estimated by the parameter estimation unit, and the second estimate value, which is the estimate value estimated by the index estimation unit, and calculates an estimate of the final failure risk index.

According to the present invention, a model equation representing a transition of a failure risk index according to a statistical distribution is constructed, and the parameter value of the model equation is statistically estimated as an estimate of the failure risk index. By doing in this way, even if there is no or little maintenance performance data of the equipment, it is possible to properly estimate the index of the risk of failure of the equipment.

1 is a block diagram showing the functional configuration of a failure risk index estimation device according to Embodiment 1 of the present invention.
2A is a diagram showing items of information stored in the FMEA result database (hereinafter referred to as DB). 2B is a diagram showing items of information stored in the work interval DB which has been determined. 2C is a diagram showing information for statistical evaluation.
3 is a diagram showing items of information stored in the first storage unit in the first embodiment.
4A is a block diagram showing a hardware configuration for realizing the function of the failure risk index estimation device according to the first embodiment. 4B is a block diagram showing a hardware configuration for executing software that realizes the function of the failure risk index estimation device according to the first embodiment.
5 is a flowchart showing the operation of the failure risk index estimation device according to the first embodiment.
6 is a view showing a relationship between a failure risk index and an intermediate evaluation index.
7 is a view showing the relationship between the evaluation items of the FMEA results and the intermediate evaluation indicators.
8 is a flow chart showing the operation of the model expression construction unit and the parameter estimation unit.
9 is a diagram showing a result of merging table data of the FMEA result DB and the previously determined work interval DB.
10 is a diagram showing the results of assigning parameters of an intermediate evaluation index to evaluation items of FMEA results.
11 is a view showing a maintenance work interval calculated from a model equation.
12 is a block diagram showing a functional configuration of a failure risk index estimation device according to Embodiment 2 of the present invention.
13A is a diagram showing items of information stored in the facility information DB. 13B is a diagram showing items of information stored in the reward performance DB. 13C is a diagram showing items of information stored in the failure performance DB.
14A is a diagram showing items of information stored in the range reduction data storage unit. 14B is a diagram showing items of information stored in the second storage unit. 14C is a diagram showing an estimate of a failure risk index.
15 is a flowchart showing the operation of the failure risk index estimation device according to the second embodiment.
16 is a flowchart showing the operation of the range reduction unit in the second embodiment.
17 is a flowchart showing the operation of the index estimation unit according to the second embodiment.
18 is a flowchart showing the operation of the merging unit in the second embodiment.

Hereinafter, in order to describe the present invention in more detail, embodiments for carrying out the present invention will be described in accordance with the accompanying drawings.

Embodiment 1.

1 is a block diagram showing the functional configuration of a failure risk index estimation device 1 according to Embodiment 1 of the present invention. The failure risk index estimation device 1 is a device for estimating the failure risk index of a facility, the FMEA result DB 2, the previously determined work interval DB 3, the index estimation unit 4, and the first storage unit 5 ). The indicator estimator 4 estimates the failure risk index of the facility based on the information A and the statistical evaluation information A read from each of the FMEA result DB 2 and the previously determined work interval DB 3. The failure risk index is information that quantifies the magnitude of the risk of a failure occurring in components constituting a facility.

The FMEA result DB 2 is a DB that stores FMEA results for each inspection item of parts constituting the equipment. In the FMEA result DB 2, for example, item information as shown in Fig. 2A is stored. In the item of "part ID", identification information of the part is set. In the item of "check item ID", identification information of the check item is set. Inspection items include, for example, appearance inspection, energization inspection, insulation inspection, and friction inspection. Both the conduction inspection and the insulation inspection can be said to be similar inspection items since they are for inspecting the electrical conduction state.

"Frequency level of failure", "Level of magnitude of impact" and "Detectability level" are FMEA evaluation items. In the item "Frequency frequency level", an evaluation level of FMEA regarding the frequency of failure is set. In the item of " influence size level ", an evaluation level of FMEA regarding the magnitude of the influence of a failure on a part is set. In the item of "detectability level", an evaluation level of FMEA regarding the ease of detection of a fault is set.

The previously determined work interval DB 3 is a DB that stores information indicating the repair work interval already determined for each inspection item of the part. In the previously determined work interval DB 3, for example, item information as shown in Fig. 2B is stored. About "part ID" and "check item ID", it is the same as that shown in FIG. 2A. In the item of "month number of work interval already determined", the number of months of the repair work interval already determined for each inspection item of the part is set.

The information A for statistical evaluation is information showing the statistical distribution used for statistical estimation of a failure risk index. In the information A for statistical evaluation, information of an item as shown in FIG. 2C is stored, for example. In the item of "statistical distribution", the type of statistical distribution used for statistical estimation of the failure risk indicator is set. The types of statistical distributions include theoretically derived distributions such as Weibull distribution, gamma distribution, and lognormal distribution.

In the item of "permissible error", the ratio of the error in which the estimated value of the failure risk index estimated by the index estimation unit 4 is allowed with respect to the standard deviation is set.

In the item of "reliability rate", the reliability rate of the failure risk index estimated by the index estimation unit 4 is set. Both the probability P1 of the result that the estimated value of the failure risk index falls within the tolerance and the probability P2 of the result of the evaluation that the estimated value of the failure risk index does not fall within the tolerance are equal to or higher than the value α set as the "confidence rate" Becomes (P1≥α, P2≥α).

In addition, since "acceptable error" and "confidence rate" are information used for the merge processing described later in the second embodiment, the information for statistical evaluation in the first embodiment may not have such information.

The index estimating section 4 includes a model equation building section 4a and a parameter estimating section 4b.

The model expression construction unit 4a constructs a model expression representing the transition (change in time) of the failure risk index based on the FMEA result read from the FMEA result DB 2 and the information A for statistical evaluation.

The parameter estimation unit 4b has the smallest difference between the maintenance work interval calculated from the model equation and the previously determined repair work interval, based on the information read from the FMEA result DB (2) and the predetermined work interval DB (3). The parameter value of the model equation is statistically estimated so as to have

The model equation is a model of the transition of the failure risk index according to the statistical distribution indicated by the statistical evaluation information A, and the parameter value of the model equation becomes the first estimate.

The first storage unit 5 stores a first estimation value that is a parameter value estimated by the index estimation unit 4. In the first storage unit 5, for example, information for each item as shown in FIG. 3 is stored. "Part ID" and "Check item ID" are the same as those shown in Fig. 2A, and "statistical distribution" is the same as shown in Fig. 2C.

In the item of "statistical distribution parameter", a parameter that defines the statistical distribution used in the statistical estimation of the first estimate is set. When the statistical distribution follows the Weibull distribution, the shape parameter γ and the scale parameter φ of the cumulative density function defining the Weibull distribution are set.

"Time scale factor", "risk weighting factor" and "safety margin" are parameters of the above model equation, and parameter values estimated by the indicator estimating section 4 are set.

The "time scale factor" is a parameter related to the rate at which the failure risk increases in the model equation. The "risk weighting factor" is a parameter relating to the degree of weighting of the failure risk in the model equation. The "safety margin" is a parameter indicating a time interval dating from the maintenance work date and time at the work interval estimated in the model equation. With this parameter, the maintenance work of parts is carried out earlier.

The FMEA result DB 2 and the previously determined work interval DB 3 are the databases 100 shown in Figs. 4A and 4B. The information stored in each of the FMEA result DB 2 and the previously determined work interval DB 3 is input to the index estimation unit 4 through the DB input / output interface 101.

The statistical evaluation information A is input to the failure risk index estimation apparatus 1 through the information input interface 102. The estimated value of the failure risk indicator is output from the failure risk indicator estimation apparatus 1 via the information output interface 103.

The first storage unit 5 may be provided in the storage device having the database 100, but may be provided in the internal memory of the processing circuit 104 shown in FIG. 4A. Note that the first storage unit 5 may be provided in the memory 105 shown in Fig. 4B.

Each function of the model expression construction unit 4a and the parameter estimation unit 4b in the failure risk index estimation apparatus 1 is realized by a processing circuit.

That is, the failure risk index estimating apparatus 1 builds a model expression showing the transition of the failure risk index based on the FMEA results read from the FMEA result DB 2 and the information A for statistical evaluation, and the FMEA result DB ( Based on the information read from 2) and the predetermined work interval DB (3), for statistically estimating the parameter value of the model equation in which the difference between the repair work interval calculated from the model equation and the predetermined repair work interval becomes the smallest. And a processing circuit.

The processing circuit may be dedicated hardware or a CPU (Central Processing Unit) that executes a program stored in memory.

When the processing circuit is dedicated hardware as shown in Fig. 4A, the processing circuit 104 is, for example, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or a combination of these are applicable.

Each of the functions of the model formula constructing section 4a and the parameter estimating section 4b may be realized by respective processing circuits, or these functions may be combined and realized by one processing circuit.

When the processing circuit is the processor 106 as shown in Fig. 4B, each function of the model expression constructing section 4a and the parameter estimating section 4b is realized by software, firmware, or a combination of software and firmware. .

Software or firmware is described as a program and stored in memory 105. The processor 106 realizes the functions of each part by reading and executing a program stored in the memory 105.

That is, the failure risk index estimating apparatus 1 includes a memory 105 for storing a program in which steps ST1 and ST2 shown in FIG. 5 are executed as a result when executed by the processor 106. In addition, these programs execute the procedures or methods of the model equation construction unit 4a and the parameter estimation unit 4b on the computer.

The memory 105 includes, for example, a nonvolatile or volatile semiconductor memory such as random access memory (RAM), read only memory (ROM), flash memory, erasable programmable read only memory (EPROM), and electrically-EPROM (EEPROM). Examples include magnetic disks, flexible disks, optical disks, compact disks, mini disks, and DVDs.

Further, for each of the functions of the model formula constructing section 4a and the parameter estimating section 4b, some may be realized by dedicated hardware, and some may be realized by software or firmware. For example, the model expression construction unit 4a realizes its function with a processing circuit as dedicated hardware, and for the parameter estimation unit 4b, the processor 106 reads and executes the program stored in the memory 105. The function may be realized by this.

In this way, the processing circuit can realize each of the above functions by hardware, software, firmware, or a combination thereof.

Next, the operation will be described.

Fig. 5 is a flowchart showing the operation of the failure risk index estimation apparatus 1, and shows a series of processes until the estimated value of the failure risk index is obtained and stored in the first storage unit 5. 6 is a diagram showing the relationship between the failure risk indicator R (t) and the intermediate evaluation indicators S, W, and M, and shows the transition of the failure risk indicator R (t) in the elapsed time after the maintenance work. 7 is a view showing the relationship between the evaluation items C, E, D of the FMEA results and the intermediate evaluation indicators S, W, M.

Hereinafter, the operation of the failure risk index estimation apparatus 1 will be described with reference to Figs. 5 and 7.

First, the index estimating unit 4 builds a model expression of the failure risk index based on the FMEA results read from the FMEA result DB 2 and the information A for statistical evaluation (step ST1).

The failure risk index R (t) changes (time changes) according to the statistical distribution f (t) as shown in the following formula (1). In the following formula (1), the time scale coefficient S is a coefficient for adjusting the change in the time direction of R (t), and is a parameter related to the rate at which the failure risk increases. The risk weighting coefficient W is a coefficient for adjusting the size of R (t), and is a parameter relating to the degree of weighting of the failure risk.

In the statistical estimation of R (t) by the indicator estimating unit 4, as shown in FIG. 6, an appropriate interval for performing a maintenance interval for a work interval in which R (t) exceeds the upper limit of "1.0", which is an allowable upper limit threshold, is performed. In the case of T _E , for safety, the work interval for carrying out the maintenance work is calculated. For example, the index estimation unit 4 sets the safety margin M, and calculates the work interval T calculated from the model equation from the following equation (2). The safety margin M is a time interval dating back from the elapsed time at the appropriate interval T _E.

In the statistical estimation of R (t) by the indicator estimator 4, the time scale factor S, the risk weighting factor W, and the safety margin M are parameters whose values fluctuate until an estimate of the failure risk indicator is obtained. . Here, these are called intermediate evaluation indicators.

When the statistical distribution f (t) follows the Weibull distribution, the statistical distribution f (t) can be expressed as a function of the cumulative density of the Weibull distribution shown in the following equation (3). Here, the shape parameter γ and the scale parameter φ are statistical distribution parameters in the cumulative density function of the Weibull distribution.

In addition, the said Formula (1) can be represented by the following Formula (4) using shape parameter (gamma) and scale parameter (phi). In the following formula (4), since the scale parameter phi appears only as a product of the time scale coefficient S, phi = 1 is used as the parameter to estimate only S.

When the elapsed time t is an appropriate interval T _E , since the failure risk index R (T _E ) = 1.0, the appropriate interval T _E can be expressed by the following equation (5) using the above equation (4). In the following formula (5), ln represents a natural logarithm.

As shown in Fig. 7, the evaluation items of the FMEA results include the frequency level of the failure, the magnitude level of the impact, and the detectability level, after which the frequency level of the failure is C and the magnitude level of the impact is E and detected. Let the probability level be D. There is a relationship shown in Fig. 7 between the evaluation items of the FMEA results and the intermediate evaluation indicators.

The frequency level C of the failure can be an index of the frequency at which a failure occurs in the component to be evaluated, and is therefore related to the time scale factor S regarding the rate of increase in the failure risk.

The magnitude of the impact level E can be an indicator of the magnitude of the impact on the component under evaluation, so the risk weighting factor W for the severity of the failure risk and the safety margin M for the degree to which repair work is advanced. Is related to both sides of

Since the detectability level D can be an indicator of the ease of detection of a failure occurring in the part to be evaluated, it is related to the safety margin M regarding the degree of accelerating maintenance work.

The index estimator 4 statistically estimates the values of the parameters S, W, M, γ, and φ of the model formula so that the difference between the maintenance work interval T calculated from the model formula and the predetermined maintenance work interval T _S is minimized. (Step ST2). Since the scale parameter φ is fixed to "1", the index estimator 4 calculates the values of the parameters S, W, M, and γ of the model formula for each part and for each inspection item, and correlates them to the parts and inspection items. It is stored in the first storage section 5.

Next, the detailed operation of the index estimation unit 4 will be described.

Fig. 8 is a flowchart showing the operation of the model expression construction unit 4a and the parameter estimation unit 4b. Hereinafter, it is assumed that the statistical distribution is a Weibull distribution.

The model expression construction unit 4a merges the FMEA result DB 2 and the table data of the previously determined work interval DB 3 (step ST1a). The table data is data composed of information for each item as shown in Figs. 2A and 2B, and among the table data, the part ID, the inspection item ID, and the side information following it are record data.

For example, in the information of the predetermined work interval DB 3 shown in Fig. 2B, the part ID and the inspection item ID and the number of the predetermined work interval months following it become information constituting the table data.

The model expression construction unit 4a reads table data from the previously determined work interval DB 3, and FMEA results having the same part ID and check item ID based on the part ID and check item ID in the table data Retrieve table data of DB (2).

The model expression construction unit 4a performs so-called merging, which combines the FMEA results of the table data specified by this search with the table data read from the previously determined work interval DB3.

The model formula construction unit 4a executes the above processing on all table data in the previously determined work interval DB 3, and generates information in which the previously determined maintenance work interval and FMEA results are combined. 9 shows this combination information. In Fig. 9, the previously determined maintenance work interval T _S is indicated by the number of months of the work interval.

Of the table data of the FMEA result DB 2, the table data that does not have the same part ID and check item ID as the table data of the previously determined work interval DB 3 is also added to the combination information shown in FIG.

The model formula construction unit 4a allocates parameters of the intermediate evaluation index related to the FMEA results according to the evaluation items and evaluation levels of the FMEA results for each part ID and for each inspection item ID (step ST2a).

The model expression constructing unit 4a assigns a parameter of a common intermediate evaluation index to an evaluation item related to the failure of the FMEA and the evaluation level in the evaluation item, among FMEA results of the combination information, to assign a model equation. To build.

In the common parameter, the same value is also set in the model equation between data having different part IDs and inspection item IDs. In this way, it is possible to evaluate the influence of failures that commonly act on different parts or inspection items.

10 is a diagram showing the results of assigning parameters of an intermediate evaluation index to evaluation items of FMEA results. For example, in FIG. 9, the frequency level C of the failure, which is an evaluation item of the FMEA result corresponding to (part ID, inspection item ID) = (EQ001, MT001), has an evaluation level of "2". Among the FMEA results of the combination information shown in Fig. 9, (part ID, check item ID) = (EQ002, MT002) is one of the ones in which the evaluation level of the failure frequency level C is "2". Expression model construction unit (4a), as shown in Figure 10, with respect to the parameters of the time scale factor S in relation to the frequency of level C of the failure, a common allocation of parameters S _2.

The magnitude level E of the influence which is the evaluation item of the FMEA result corresponding to (part ID, inspection item ID) = (EQ001, MT001) has an evaluation level of "3".

Among the FMEA results of the above combination information, (part ID, check item ID) = (EQ001, MT002) is one of the evaluation levels of the influence level level E of "3". As shown in Fig. 10, the model formula construction unit 4a assigns a common parameter W ₃ to the parameter of the risk weighting coefficient W related to the magnitude level E of the influence.

In the data of (EQ001, MT001) and the data of (EQ001, MT002), the evaluation level D, which is an evaluation item of the FMEA result, also has an evaluation level of "3". For this reason, the model expression construction unit 4a assigns common parameters M _{3 and 3} to the parameters of the safety margin M related to the detectability level D, as shown in FIG. 10.

When the model expression construction unit 4a completes the assignment of the parameters of the intermediate evaluation index to the data of (part ID, check item ID) = (EQ001, MT001), the equations (2) to (5) Based on this, the work interval T, the failure risk index R (t), and the appropriate interval T _E are constructed as model models of the failure risk index.

Since the parameters of the intermediate evaluation index for the data of (EQ001, MT001) are S ₂ , W ₃ , M _{3, 3} , the model equation is as follows.

The safety margin M may be expressed as the product M _E x M _D of the coefficient factor M _E by the magnitude level E of the influence and the coefficient factor M _D by the detectability level D. Thereby, since the safety margin M becomes a fixed value, the number of parameters to be estimated can be reduced.

Next, the parameter estimating section 4b calculates the work interval T from the model expression based on the parameters assigned by the model expression building section 4a, and the difference between the work interval T and the predetermined work interval T _S The minimum parameter is statistically estimated (step ST3a).

Since the statistical distribution f (t) follows the Weibull distribution, the work interval T is obtained in the form of a variable determined by the parameters of the intermediate evaluation index, as shown in FIG. 11. In FIG. 11, Z _i is a parameter represented by the following formula (6).

The parameter estimator 4b, for all the part IDs and the inspection item IDs in the combination information, the parameters S and W of the intermediate evaluation index such that the sum of squares of the difference between the work interval T and the already determined work interval T _S is minimum. , M and shape parameter γ are estimated. Moreover, as described above, the scale parameter φ is "1".

As a method for estimating these parameters, for example, a conjugate direction method is used. However, the method is not limited to the conjugate direction method as long as it is a method capable of estimating a parameter in which the error with the predetermined working interval T _S is minimum.

The parameter estimation unit 4b classifies the results estimated as described above for each part ID and for each inspection item ID, and stores the result in the first storage unit 5 in association with the parts ID and inspection item ID (step ST4a) ). Since the statistical distribution f (t) conforms to the Weibull distribution, the "Wye distribution" is set in the item of "Statistic distribution" shown in Fig. 3, and the value of the shape parameter γ is set in the item of "Statistic distribution parameter". Is set.

The parameter estimating unit 4b may use information used in statistical estimation of parameter values for one of the check items similar to each other for statistical estimation of parameter values for the other between check items. For example, since both the conduction inspection and the insulation inspection are for inspecting the electrical conduction state, it can be said to be a similar inspection item. Therefore, the parameter estimation unit 4b uses the information used in the statistical estimation of the parameter value for the energization test for statistical estimation of the parameter value for the insulation test. By doing in this way, the information used for statistical estimation can be reused, and the processing load required for estimation can be reduced.

As described above, in the failure risk index estimating apparatus 1 according to the first embodiment, the model expression construction unit 4a reads the FMEA results and statistical evaluation information A read from the FMEA results DB 2. On the basis of this, a model equation is constructed to show the transition of failure risk indicators according to the statistical distribution. Based on the information read by the parameter estimation unit 4b from the FMEA result DB (2) and the predetermined work interval DB (3), the difference between the repair work interval calculated from the model formula and the previously determined repair work interval is the smallest. Ji estimates the parameter values of the model equation statistically. By configuring in this way, even if there is no or little maintenance performance data of the equipment, it is possible to appropriately estimate the index of the risk of failure of the equipment.

In the failure risk index estimating apparatus 1 according to the first embodiment, the model expression construction unit 4a, when the evaluation items related to the failures of the FMEA and the evaluation levels in the evaluation items are the same, are applied to the evaluation items. Common parameters are assigned to the parameters of the associated model expression. By doing in this way, it is possible to evaluate the influence of failures that commonly act on different parts or inspection items.

Further, in the failure risk index estimation apparatus 1 according to the first embodiment, the parameter estimation unit 4b uses information used in statistical estimation of parameter values for one of the check items similar to each other, between check items. It is used for statistical estimation of parameter values for the other side of.

By doing in this way, the information used for statistical estimation can be reused, and the processing load required for estimation can be reduced.

Embodiment 2.

12 is a block diagram showing a functional configuration of a failure risk index estimation device 1A according to Embodiment 2 of the present invention. In Fig. 12, the same components as those in Fig. 1 are given the same reference numerals, and description is omitted. The failure risk index estimation apparatus 1A, in addition to the configuration shown in the first embodiment, facility information DB 6, maintenance performance DB 7, failure performance DB 8, range reduction unit 9, range reduction A data storage section 10, an index estimation section 11, a second storage section 12, and a merge section 13 are provided.

The facility information DB 6 is a DB that stores facility information including facilities, parts constituting the facility, and inspection items for each part. In the facility information DB 6, for example, item information as shown in Fig. 13A is stored. In the item of "facility ID", equipment identification information is set. About "part ID" and "check item ID", it is the same as that shown in FIG. 2A.

In the item of "repair start date and time", the date and time when the repair contract for each inspection item of the part is started is set. In addition, during the maintenance contract, the date and time of performing the maintenance work individually performed is the date and time of the maintenance work described later using Fig. 13B.

The maintenance performance DB 7 is a DB which stores performance data of maintenance work for each inspection item of parts constituting the equipment. In the maintenance performance DB 7, information of items as shown in Fig. 13B is stored. In the item of "reward performance ID", identification information of the performance data of the maintenance work is set.

About "facility ID", "part ID" and "inspection item ID", it is the same as that shown in FIG. 13A and FIG. 2A. In the item "Date and time of maintenance work", the date and time of maintenance work for each part is set.

The failure performance DB 8 is a DB that stores failure performance data for each component constituting the equipment. In the failure performance DB 8, information of items as shown in Fig. 13C is stored. In the item of "failure performance ID", identification information of the failure performance data is set. About "facility ID" and "part ID", it is the same as that shown in FIG. 13B. In the item "Date of occurrence of failure", the date and time when the component failed is set. In the item of " related inspection item ID ", identification information of the inspection item related to the generated failure is set.

The range reduction unit 9 classifies the information stored in the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 into information corresponding to each FMEA result of the FMEA result DB 2. .

For example, the range reduction unit 9 aggregates the intervals of failure-free after performing maintenance work for each maintenance performance data, and classifies information indicating the aggregated failure intervals for each FMEA result, thereby reducing the range reduction data storage unit (10) Remember.

The range reduction data storage unit 10 is a DB that stores information classified by the range reduction unit 9. In the range reduction data storage unit 10, information of items as shown in Fig. 14A is stored.

In Fig. 14A, "Frequency level of failure", "Amplitude level of impact" and "Detectability level" are evaluation items of FMEA, and are the same as those shown in Fig. 2A.

"Part ID" and "Check item ID" are the same as those shown in Fig. 2A. The "date and time of maintenance work" is the same as that shown in Fig. 13B.

In the items of "No faulty continuation month", the number of months that have reached the next repair work without fault after the repair work, the number of months until a failure occurs after the repair work, and the number of months until the fault has reached the present after repair work. Either is set. In the item of "failure occurrence flag", a value indicating whether or not a component has failed is set. For example, "1" is set when a failure occurs in the component, and "0" is set if no failure occurs.

The indicator estimation unit 11 estimates a second estimate of the failure risk indicator according to the relationship between the performance data of the maintenance work and the performance data of the failure, based on the information classified by the range reduction unit 9.

For example, the index estimator 11 reads information corresponding to the FMEA result to be processed from the range reduction data storage unit 10 and statistically sets a failure risk index for each component and for each inspection item based on the read information. Estimate. The estimated value of the failure risk index obtained by this estimation is stored in the second storage unit 12 together with the actual number of data used for the estimation.

The second storage unit 12 stores the estimated value of the failure risk index estimated by the index estimation unit 11 for each part and for each inspection item. In the second storage unit 12, item information as shown in Fig. 14B is stored. In Fig. 14B, "part ID" and "check item ID" are the same as those shown in Fig. 2A. "Statistic distribution" and "Statistic distribution parameter" are the same as those shown in FIG. 3. The "time scale factor", "risk weighting factor" and "safety margin" are parameters that become the second estimate of the failure risk index, and are the same as those shown in FIG. 3. In the item of "actual data number", the number of data used for statistical estimation of the failure risk index by the index estimator 11 is set.

The merging unit 13 divides the first estimate estimated by the parameter estimator 4b and the second estimate estimated by the indicator estimator 11 to calculate an estimate of the final failure risk index.

For example, the merging unit 13 divides the first estimate and the second estimate according to the number of data assumed in the estimation of the first estimate and the actual number of data used for the estimation of the second estimate, and estimates the final failure risk index. Calculate Information B indicating the estimated value of the failure risk index is output from the merging unit 13.

The information B indicating the estimated value of the failure risk index is composed of the information of the items shown in FIG. 14C. In FIG. 14C, "part ID" and "check item ID" are the same as those shown in FIG. 2A. "Statistic distribution" and "Statistic distribution parameter" are the same as those shown in FIG. 3. The "time scale factor", "risk weighting factor" and "safety margin" are parameters that become the second estimate of the failure risk index, and are the same as those shown in FIG. 3.

FMEA result DB (2) in the failure risk index estimation apparatus 1A, the predetermined work interval DB (3), facility information DB (6), maintenance performance DB (7) and failure performance DB (8) are It is the database 100 shown in 4a and 4b. The information stored in each of the FMEA result DB 2 and the previously determined work interval DB 3 is input to the index estimation unit 4 through the DB input / output interface 101.

The information stored in each of the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 is input to the range reduction unit 9 through the DB input / output interface 101.

The statistical evaluation information A is input to the failure risk index estimation apparatus 1A through the information input interface 102. The information B indicating the estimated value of the final failure risk index is output from the merging unit 13 via the information output interface 103.

Although it is conceivable that the first storage unit 5, the range reduction data storage unit 10, and the second storage unit 12 are provided in a storage device having a database 100, the processing circuit 104 shown in FIG. 4A ) May be provided in the internal memory. Further, the first storage unit 5, the range reduction data storage unit 10, and the second storage unit 12 may be provided in the memory 105 shown in FIG. 4B.

Each function of the indicator estimating section 4, the range reducing section 9, the indicator estimating section 11 and the merging section 13 in the failure risk index estimating apparatus 1A is realized by a processing circuit.

That is, the failure risk index estimation apparatus 1A is provided with a processing circuit for performing processing in the functions of the above-described respective parts. The processing circuit may be dedicated hardware or a CPU that executes a program stored in memory.

When the processing circuit is dedicated hardware as shown in Fig. 4A, the processing circuit 104 is, for example, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. This is true.

Each of the functions of the index estimating section 4, the range reducing section 9, the index estimating section 11, and the merging section 13 may be realized by respective processing circuits, and these functions are combined into one processing circuit. You may realize.

When the processing circuit is the processor 106 as shown in Fig. 4B, each function of the index estimating section 4, the range reducing section 9, the index estimating section 11 and the merging section 13 is software, It is realized by firmware, or a combination of software and firmware.

Software or firmware is described as a program and stored in memory 105. The processor 106 realizes the functions of each part by reading and executing a program stored in the memory 105.

That is, the failure risk index estimation apparatus 1A includes a memory 105 for storing a program in which the processing from step ST1b to step ST8b shown in Fig. 15 is executed as a result when executed by the processor 106. .

In addition, these programs execute the procedures or methods of the index estimating section 4, the range reducing section 9, the index estimating section 11, and the merging section 13 on the computer.

For each function of the index estimating section 4, the range reducing section 9, the index estimating section 11, and the merging section 13, some may be realized by dedicated hardware, and some may be realized by software or firmware. For example, the index estimation unit 4 and the range reduction unit 9 realize their functions with processing circuits as dedicated hardware, and for the index estimation unit 11 and the merging unit 13, the processor 106 has memory The function may be realized by reading and executing the program stored in (105). In this way, the processing circuit can realize each of the above functions by hardware, software, firmware, or a combination thereof.

Next, the operation will be described.

15 is a flowchart showing the operation of the failure risk index estimating apparatus 1A, and shows a series of processes until the first and second estimates of the failure risk index are calculated and the final estimate is output.

The range reduction unit 9 classifies the information stored in the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 into information corresponding to each FMEA result (step ST1b).

The information classified by the range reduction unit 9 is stored in the range reduction data storage unit 10.

The index estimator 11 reads information corresponding to the FMEA result to be processed from the range reduction data storage unit 10 and statistically estimates a failure risk index for each component and for each inspection item based on the read information. (Step ST2b).

The index estimation unit 11 stores the second estimated value and actual data number obtained in the estimation in the second storage unit 12 in association with the part ID and the inspection item ID (step ST3b).

If all FMEA results stored in the FMEA result DB 2 are not processed (step ST4b; No), the processing from step ST1b is repeated.

When all FMEA results stored in the FMEA result DB 2 are processed (step ST4b; YES), the process proceeds to step ST5b.

In step ST5b, the index estimation unit 4 estimates the parameter value that is the first estimate in the same manner as in the first embodiment.

The index estimation unit 4 stores the estimated first estimated value in the first storage unit 5 in association with the part ID and the inspection item ID (step ST6b).

The merging unit 13 reads the first estimate value stored in the first storage unit 5 and the second estimate value stored in the second storage unit 12, and divides the read first estimate value and the second estimate value. Then, an estimate of the final failure risk index is calculated (step ST7b). Thereafter, the merging unit 13 outputs information B indicating the estimated value of the calculated failure risk index (step ST8b).

15, the case where the estimation of the second estimation value by the index estimation unit 11 is performed before the estimation of the first estimation value by the index estimation unit 4 is shown, but is not limited to this.

For example, the estimation of the first estimation value by the index estimation unit 4 may be performed before the estimation of the second estimation value by the index estimation unit 11. Further, the estimation of the first estimation value by the index estimation unit 4 and the estimation of the second estimation value by the index estimation unit 11 may be performed in parallel.

Next, the operation of the range reduction unit 9 will be described in detail.

Fig. 16 is a flowchart showing the operation of the range reduction unit 9, and the information stored in the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 is classified to reduce the range data storage unit 10. ) Shows the series of processes until the memory is remembered.

First, the range reduction unit 9 merges each table data of the FMEA result DB 2, the facility information DB 6, and the maintenance performance DB 7 (step ST1c).

For example, the range reduction unit 9 has a repair performance DB having the same facility ID, part ID, and inspection item ID based on the facility ID, part ID, and inspection item ID of the table data read from the facility information DB 6. Search the table data in (7). The range reduction unit 9 merges the information set in the item "Date of maintenance work" of the table data specified by this search with the table data read from the facility information DB 6.

Further, the range reduction unit 9 does not delete the record data of the equipment ID, part ID, and inspection item ID used for the search when there is no maintenance performance data of the same equipment ID, part ID, and inspection item ID as the facility information. It is left in the table data after merging.

Subsequently, the range reduction unit 9 of the FMEA result DB 2 having the same facility ID, part ID, and inspection item ID based on the facility ID, part ID, and inspection item ID in the table data after merging. Retrieve table data. The range reduction unit 9 merges the FMEA results in the table data specified by this search into the table data after the merge.

The range reduction unit 9 performs the above processing on all table data in the facility information DB 6 to generate information in which facility information and maintenance performance data are classified for each FMEA result.

Next, the range reduction unit 9 reduces the range only from the record data corresponding to the FMEA result to be evaluated among the merged table data as described above (step ST2c).

The range reduction unit 9 adds items of "no faulty continuation month" and "fault occurrence flag" to the record data of the merged table data.

In step ST3c, the range reduction unit 9 performs repair work on the parts corresponding to this part ID based on the facility ID and part ID corresponding to the FMEA result to be evaluated, and then proceeds to the next repair work. The failure performance data that occurred most quickly in the meantime is retrieved from the failure performance DB 8. In addition, if the maintenance work has not been performed, the failure performance data that has occurred most quickly from the date of the maintenance start to the next maintenance work is retrieved.

The range reduction unit 9 calculates the number of faultless continuous months in which no failure has occurred in the part based on the failure performance data of the search result, sets the calculated number of months in the item of "Faultless Continued Months", and fails "1" which is a value indicating that this has occurred is set in the item of "Failure occurrence flag".

On the other hand, if there is no failure result in the above search, the range reduction unit 9 calculates the time interval until the next maintenance work date and time, sets this number of months to the item "No faulty continuous months", and no failure occurs. The value "0" indicating that it is not used is set in the item of "Failure occurrence flag".

In addition, when the date and time of the maintenance work is not determined next, the interval to the current time is set in the item of "No faulty continuous months".

The range reduction unit 9 performs the above processing on all record data in the table data merged in step ST1c. As a result, information classified by facility information, maintenance performance data, and failure performance data for each FMEA result is generated.

The range reduction unit 9 stores the table data of the processing result in the range reduction data storage unit 10 (step ST4c). As shown in Fig. 14A, the table data includes "Frequency frequency level", "Effect size level", "Detectability level", "Facility ID", "Part ID", "Check item ID", and "Maintenance" It is data consisting of items such as "date and time of execution of work", "number of months without failure" and "fault occurrence flag".

Next, the operation of the index estimation unit 11 will be described in detail.

17 is a flowchart showing the operation of the indicator estimating unit 11, and shows a series of processes from estimating the second estimation value until it is stored in the second storage unit 12.

In step ST1d, the index estimation unit 11 retrieves record data having the same component ID and inspection item ID to be processed from the table data stored in the range reduction data storage unit 10. The index estimation unit 11 calculates the number of record data whose value set in the item of "No faulty continuous month" in searched record data is less than or equal to the elapsed time t (t = 1, 2, ...) after the maintenance work. do. The number of record data calculated in this way is the total number of parts according to the number of months elapsed since the maintenance work.

The index estimation unit 11 retrieves record data in which "1" is set to the item of "fault occurrence flag" from the table data stored in the range reduction data storage unit 10.

The index estimation unit 11 calculates the number of failures according to the number of months after the maintenance work, for each part ID and for each inspection item ID, based on the retrieved record data (step ST2d).

Next, the index estimation unit 11 calculates the performance failure rate, which is a value obtained by dividing the number of failures according to the number of months elapsed after the maintenance work by the total number (step ST3d).

Subsequently, the index estimator 11 specifies a statistical distribution that approximates the trend of the performance failure rate calculated in step ST3d, and statistically estimates the parameter values of the model equations according to the statistical distribution (step ST4d). For example, the time scale coefficient S and the shape parameter γ approximated by the failure risk index R (t) when the transition of the performance failure rate is set to the risk weighting factor W and 1 in the above formula (4) are estimated.

It is conceivable to use the conjugate direction method for parameter estimation, but it may be an existing method for estimating the parameter of the Weibull distribution.

The value of the risk weighting factor W is set for each FMEA result. For example, a value received from the user may be set, or a value of a coefficient in which the difference between the loss amount and the failure rate at the time of failure generated from the performance data is the smallest may be set. The value of the safety margin M may be set by a value received from the user, or M = 0.

Moreover, although the Weibull distribution was used as a statistical distribution, it may be a statistical distribution such as a gamma distribution or a lognormal distribution, and may be a statistical distribution with minimal error from performance data.

The index estimation unit 11 checks whether there is an unprocessed combination among the combination of the part ID and the check item ID of the table data stored in the range reduction data storage unit 10 (step ST5d).

If there is an unprocessed combination among the combination of the part ID and the check item ID of the table data stored in the range reduction data storage unit 10 (step ST5d; No), the process returns to step ST1d, and the above-described processing is repeated. .

When all combinations of the part ID and the inspection item ID are processed (step ST5d; YES), the index estimating unit 11 associates the estimated second value with a second estimation value with the part ID and the inspection item ID, and stores the second storage unit. It is stored in (12) (step ST6d).

Since the statistical distribution conforms to the Weibull distribution, the "Wye distribution" is set in the item of "Statistic distribution" shown in Fig. 14B, and the value of the shape parameter γ is set in the item of "Statistic distribution parameter".

Next, the operation of the merging unit 13 will be described in detail.

18 is a flowchart showing the operation of the merging unit 13, and shows a series of processes from the first estimate and the second estimate until the final failure risk index is calculated and output. The merging unit 13 reads the part ID and the inspection item ID to be processed, the second estimated value corresponding to them, and the actual number of data from the second storage unit 12 (step ST1e).

Next, the merging unit 13 reads the part ID and the inspection item ID to be processed and the first estimate corresponding to them from the first storage unit 5 (step ST2e).

Of the first estimates stored in the first storage unit 5, records that do not have the same part ID and inspection item ID as the part IDs and inspection item IDs corresponding to the second estimation values read from the second storage unit 12 About the data, it is read from the first storage section 5 as it is.

The number of actual data set in the record data read from the second storage unit 12 is set to N _A , the total number of parts for each elapsed time after maintenance work is always set to N _A , and the number of failures is N _A × Assume R _A (t). The merging unit 13 finds N _I, which is the number of data assumed in the statistical estimation of the first estimate, in the record data having the same part ID and check item ID read from the first storage unit 5 (step) ST3e).

For example, the merging unit 13 calculates N _I from the following equation (7) using the tolerance Δ and the confidence factor α in the information A for statistical evaluation. In the following formula (7), z (α) represents 100 α% above the standard normal distribution. When Δ = 0.1 and α = 0.99 (99%), z (α) = 2.326 and N _I = 2168.

Article according to one of the record data having the same component ID and the inspection item ID read out from the storage unit 5, the maintenance elapsed total number of parts of each time after the operation, N _I always calculated by the equation (7) It is assumed that the number of failures is N _I × R _I (t).

The merging unit 13 has a failure risk index R _A (t) indicated by the record data read from the second storage unit 12 and the same part ID and check item ID read from the first storage unit 5. The failure risk index R _I (t) indicated by the record data is divided (step ST4e).

The merging unit 13 breaks down by setting the total number of parts for each elapsed time after repair work to N _A + N _I and the number of failures to N _A × R _A (t) + N _I × R _I (t). The risk index R _F (t) is again estimated statistically. As the estimation method, for example, the conjugate direction method is used. The processing up to this point is the separation process. In this way, the merging unit 13 uses N _I, which is the number of data assumed in the statistical estimation of the first estimate, as an ordinance with the second estimate. Thereby, the 1st estimated value and the 2nd estimated value can be appropriately divided | segmented.

The merging unit 13 checks whether or not there is a combination of an inseparable part ID and a check item ID among information stored in each of the first storage unit 5 and the second storage unit 12 (step ST5e) ).

If there is a combination of an inseparable part ID and an inspection item ID among the information stored in each of the first storage unit 5 and the second storage unit 12 (step ST5e; No), step is performed for the unprocessed combination The process from ST1e is repeated.

In the case where the combination of all the part IDs and inspection item IDs is divided (step ST5e; YES), the merging unit 13 is information indicating the estimated value of the final failure risk index from the parameter value of the failure risk index R _F (t). B is generated and output (step ST6e).

As described above, in the failure risk index estimation apparatus 1A according to the second embodiment, the range reduction unit 9 includes the equipment information DB 6, the maintenance performance DB 7, and the failure performance DB 8 ) Is classified as information corresponding to each FMEA result. The indicator estimator 11 estimates the estimated value of the failure risk indicator according to the relationship between the performance data of the maintenance work and the performance data of the failure, based on the information classified by the range reduction unit 9. The merging unit 13 divides the first estimate estimated by the indicator estimating unit 4 and the second estimate estimated by the indicator estimating unit 11 to calculate an estimate of the final failure risk index. By configuring in this way, even if there is little data on the maintenance performance of the equipment, it is possible to appropriately estimate the index of the risk of failure of the equipment.

In addition, in the failure risk index estimating apparatus 1A according to the second embodiment, the merging unit 13 uses the number of data assumed in the statistical estimation of the first estimation value to divide the second estimate value. By configuring in this way, the first estimate value and the second estimate value can be appropriately divided.

In addition, within the scope of the present invention, the present invention can freely combine each embodiment, or modify any component of each embodiment, or omit any component in each embodiment.

(Industrial availability)

The failure risk index estimation apparatus according to the present invention can be suitably applied to various mechanical systems, for example, because it is possible to appropriately estimate the risk of a failure in the facility even when there is no or little maintenance performance data of the facility.

1, 1A: Failure risk indicator estimation device
2: FMEA result DB
3: DB with the work interval already determined
4, 11: indicator estimation unit
4a: Model expression building unit
4b: parameter estimation unit
5: first memory
6: Equipment information DB
7: Remuneration performance DB
8: Breakdown performance DB
9: Range reduction part
10: Range reduction data storage
12: second memory
13: merge unit
100: database
101: DB input / output interface
102: information input interface
103: information output interface
104: processing circuit
105: memory
106: processor

Claims (7)

A model formula showing the transition of the failure risk indicator according to the statistical distribution, based on the information showing the failure mode effect analysis result for each inspection item of the components constituting the equipment and the statistical distribution used for estimating the failure risk indicator. And a model expression building unit to build
The maintenance work interval calculated from the model formula and the previously determined maintenance work, based on the information indicating the failure mode effect analysis result for each inspection item of the parts constituting the equipment and the information indicating the maintenance work interval already determined for each inspection item of the part. A parameter estimator for statistically estimating the parameter value of the model equation in which the difference in the work interval is the smallest;
The equipment, the equipment information including the components and the inspection items for each component, the performance data of the maintenance work for each inspection item of the components constituting the equipment, and the failure data for each component constituting the equipment are in failure mode. A range reduction unit to classify the information into corresponding information for each effect analysis result;
An indicator estimator for estimating the estimated value of the failure risk indicator according to the relationship between the performance data of the maintenance work and the performance data of the failure, based on the information classified by the range reduction unit;
A merger that divides the first estimate, which is the parameter value estimated by the parameter estimator, and the second estimate, which is the estimate estimated by the indicator estimator, to calculate the final estimate of the failure risk index.
Failure risk index estimation apparatus characterized in that it comprises a.
According to claim 1,
The merging unit is a failure risk index estimating apparatus, characterized in that the number of data assumed in the statistical estimation of the first estimate is used to divide the second estimate.
According to claim 1,
The model expression constructing unit is characterized in that, when the evaluation item related to the failure of the failure mode effect analysis and the evaluation level in the evaluation item are the same, the common parameter is assigned to the parameter of the model expression related to the evaluation item. Failure risk indicator estimation device.
According to claim 1,
The parameter estimating unit is a failure risk index estimating apparatus characterized in that the information used in the statistical estimation of the parameter values for one of the items between the check items similar to each other for statistical estimation of the parameter values for the other items between the check items.
Based on the information representing the failure mode effect analysis result for each inspection item of the components constituting the equipment and the statistical distribution used for the estimation of the failure risk indicator, the failure risk indicator according to the statistical distribution Steps to build a model expression representing the trend,
The parameter estimation unit calculates the maintenance work interval calculated from the model formula based on the information indicating the failure mode effect analysis result for each inspection item of the parts constituting the equipment and the information indicating the maintenance work interval already determined for each inspection item of the part. A step of statistically estimating a parameter value of the model equation in which the difference between the predetermined maintenance work intervals becomes the smallest;
The range reduction unit includes facility information including equipment, parts constituting the equipment, and inspection items for each component, performance data of maintenance work for each maintenance item of the components constituting the equipment, and performance data of failures for each component constituting the equipment. The step of classifying the information into corresponding information for each failure mode effect analysis result,
A step of estimating the estimated value of the failure risk index according to the relationship between the performance data of the maintenance work and the performance data of the failure, based on the information classified by the range reduction unit, the indicator estimation unit;
The merger divides the first estimate value, which is a parameter value estimated by the parameter estimator, and the second estimate value, which is an estimate estimated by the indicator estimator, and calculates a final estimate of the failure risk index.
Failure risk index estimation method characterized in that it comprises a. delete delete

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